Genetically encoded biosensors enabled high-throughput screening of microbial cell factories

Abstract

Genetically encoded biosensors provide powerful tools for coupling desired phenotypes to detectable outputs and have been extensively developed to detect a wide range of natural and unnatural products. When integrated with diverse high-throughput screening (HTS) approaches, these biosensors enable efficient product-driven screening across various throughputs, thereby expediting the engineering and optimization of microbial cell factories to produce various target compounds. For effective HTS of microbial cell factories, biosensors need to possess certain crucial characteristics. The performance features of biosensors significantly influence their application potential in HTS and can be precisely engineered through synthetic biology strategies. Furthermore, to ensure biosensor-driven HTS, additional engineering and optimizations of the biosensors are often required to increase the success rate and reduce false positives in the screening process. This review discusses the essential features of genetically encoded biosensors designed for HTS and then summarizes the latest advances in biosensor engineering for HTS purposes via synthetic biology strategies. Following this, the challenges and optimization of biosensors to adapt to different HTS processes are also discussed and exemplified. Finally, the key concerns and research prospects of developing biosensors for HTS applications are highlighted. Overall, this review provides comprehensive guidance on the engineering of genetically encoded biosensors and their applications in HTS for developing microbial cell factories to produce diverse target compounds.

Keywords

Genetically encoded biosensor / High-throughput screening / Microbial cell factory / Fluorescence-activated cell sorting / Fluorescence-activated droplet sorting / Synthetic biology / Artificial intelligence

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Jin Wang: Writing - review & editing, Writing - original draft, Visualization, Data curation. Xueyan Liu: Data curation. Longqian Zhao: Data curation. Yue Zhang: Writing - review & editing, Writing - original draft, Visualization, Funding acquisition, Data curation, Conceptualization. Meng Wang: Writing - review & editing, Supervision, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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